JACIII Vol.27 No.1 pp. 12-18
doi: 10.20965/jaciii.2023.p0012

Research Paper:

Digital Twin Concept Utilizing Electrical Resistivity Tomography for Monitoring Seawater Intrusion

Joseph Aristotle R. De Leon*,†, Ronnie S. Concepcion II*,***, Robert Kerwin C. Billones*,***, Jonah Jahara G. Baun**, Jose Miguel F. Custodio*, Ryan Rhay P. Vicerra*,***, Argel A. Bandala**,***, and Elmer P. Dadios*,***

*Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

***Center for Engineering and Sustainable Development Research, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

April 4, 2022
June 2, 2022
January 20, 2023
digital twin, electrical resistivity tomography, process modeling, seawater intrusion
Digital Twin Concept Utilizing Electrical Resistivity Tomography for Monitoring Seawater Intrusion

Digital twin concept for SWI monitoring

Electrical resistivity tomography (ERT) has been seen as an appropriate instrument in several works to monitor and aid in the control of seawater intrusion (SWI) in coastal groundwater systems. This study seeks to discuss the synthesis of a digital twin that couples information between the physical space through ERT as a monitoring sensor and the digital space using SWI simulations to accurately model the behavior of SWI in the present and future settings. To showcase the concept, a Python-based simulation was presented that shows (a) the joint forward modeling-simulation scheme for calculating expected ERT apparent resistivity values from simulated SWI and (b) the calibration of the digital coastal aquifer system through genetic algorithm to accurately match the outputs of the SWI simulations with the ERT measurements.

Cite this article as:
J. Leon, R. II, R. Billones, J. Baun, J. Custodio, R. Vicerra, A. Bandala, and E. Dadios, “Digital Twin Concept Utilizing Electrical Resistivity Tomography for Monitoring Seawater Intrusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.1, pp. 12-18, 2023.
Data files:
  1. [1] Q. Guo et al., “Experiment and numerical simulation of seawater intrusion under the influences of tidal fluctuation and groundwater exploitation in coastal multilayered aquifers,” Geofluids, Vol.2019, Article No.2316271, 2019.
  2. [2] M. S. L. Insigne and G.-S. Kim, “Saltwater intrusion modeling in the aquifer bounded by Manila bay and Parañaque river, Philippines,” Environ. Eng. Res., Vol.15, No.2, pp. 117-121, 2010.
  3. [3] M. B. Cardenas et al., “Devastation of aquifers from tsunami-like storm surge by supertyphoon Haiyan,” Geophys. Res. Lett., Vol.42, No.8, pp. 2844-2851, 2015.
  4. [4] P.-S. Huang and Y.-C. Chiu, “A simulation-optimization model for seawater intrusion management at Pingtung coastal area, Taiwan,” Water, Vol.10, No.3, Article No.251, 2018.
  5. [5] S. Vann et al., “Delineation of seawater intrusion using Geo-electrical survey in a coastal aquifer of Kamala beach, Phuket, Thailand,” Water, Vol.12, No.2, Article No.506, 2020.
  6. [6] I. Lovrinović et al., “Groundwater monitoring systems to understand sea water intrusion dynamics in the Mediterranean: The Neretva valley and the southern Venice coastal aquifers case studies,” Water, Vol.13, No.4, Article No.561, 2021.
  7. [7] M. S. Hussain et al., “Management of seawater intrusion in coastal aquifers: A review,” Water, Vol.11, No.12, Article No.2467, 2019.
  8. [8] A. R. Costall et al., “Groundwater throughflow and seawater intrusion in high quality coastal aquifers,” Sci. Rep., Vol.10, Article No.9866, 2020.
  9. [9] A. N. Pedersen et al., “Living and prototyping digital twins for urban water systems: Towards multi-purpose value creation using models and sensors,” Water, Vol.13, No.5, Article No.592, 2021.
  10. [10] A. Fiori et al., “Groundwater contaminant transport: Prediction under uncertainty, with application to the MADE transport experiment,” Front. Environ. Sci., Vol.7, 2019.
  11. [11] J. Ma et al., “Spatial characterization of seawater intrusion in a coastal aquifer of northeast Liaodong bay, China,” Sustainability, Vol.11, No.24, Article No.7013, 2019.
  12. [12] M. S. Al-Khafaji, “Deterministic methodology for determining the optimal sampling frequency of water quality monitoring systems,” Hydrology, Vol.6, No.4, Article No.94, 2019.
  13. [13] J. Beaujean et al., “Calibration of seawater intrusion models: Inverse parameter estimation using surface electrical resistivity tomography and borehole data,” Water Resour. Res., Vol.50, No.8, pp. 6828-6849, 2014.
  14. [14] J.-C. Comte and O. Banton, “Cross-validation of geo-electrical and hydrogeological models to evaluate seawater intrusion in coastal aquifers,” Geophys. Res. Lett., Vol.34, No.10, 2007.
  15. [15] A. Palacios et al., “Time-lapse cross-hole electrical resistivity tomography (CHERT) for monitoring seawater intrusion dynamics in a Mediterranean aquifer,” Hydrol. Earth Syst. Sci., Vol.24, No.4, pp. 2121-2139, 2020.
  16. [16] V. Bouzaglou et al., “Ensemble Kalman filter assimilation of ERT data for numerical modeling of seawater intrusion in a laboratory experiment,” Water, Vol.10, No.4, Article No.397, 2018.
  17. [17] B. Zhou, “Electrical resistivity tomography: A subsurface-imaging technique,” Applied Geophysics with Case Studies on Environmental, Exploration and Engineering Geophysics, 2019.
  18. [18] M. Singh et al., “Digital Twin: Origin to Future,” Appl. Syst. Innov., Vol.4, No.2, Article No.36, 2021.
  19. [19] T. Zhang et al., “A digital twin for unconventional reservoirs: A multiscale modeling and algorithm to investigate complex mechanisms,” Geofluids, Vol.2020, Article No.8876153, 2020.
  20. [20] K. S. Lari, G. B. Davis, and J. L. Rayner, “Towards a digital twin for characterising natural source zone depletion: A feasibility study based on the Bemidji site,” Water Res., Vol.208, Article No.117853, 2022.
  21. [21] E. Abarca et al., “Anisotropic dispersive Henry problem,” Adv. Water Resour., Vol.30, No.4, pp. 913-926, 2007.
  22. [22] C. Rücker, T. Günther, and F. M. Wagner, “pyGIMLi: An open-source library for modelling and inversion in geophysics,” Comput. Geosci., Vol.109, pp. 106-123, 2017.
  23. [23] M. Bakker et al., “Scripting MODFLOW model development using Python and FloPy,” Groundwater, Vol.54, No.5, pp. 733-739, 2016.
  24. [24] “PyGAD–Python Genetic Algorithm!–PyGAD 2.16.1 documentation.” [Accessed January 31, 2022]

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Last updated on Feb. 08, 2023